16317

Real-Time Stochastic Kinodynamic Motion Planning via Multiobjective Search on GPUs

Brian Ichter, Edward Schmerling, Ali-akbar Agha-mohammadi, Marco Pavone
Stanford University, Department of Aeronautics and Astronautics, Stanford, CA, 94305
arXiv:1607.06886 [cs.RO], (23 Jul 2016)
@article{ichter2016realtime,

   title={Real-Time Stochastic Kinodynamic Motion Planning via Multiobjective Search on GPUs},

   author={Ichter, Brian and Schmerling, Edward and Agha-mohammadi, Ali-akbar and Pavone, Marco},

   year={2016},

   month={jul},

   archivePrefix={"arXiv"},

   primaryClass={cs.RO}

}

In this paper we present the PUMP (Parallel Uncertainty-aware Multiobjective Planning) algorithm for addressing the stochastic kinodynamic motion planning problem, whereby we seek a low-cost, dynamically-feasible motion plan subject to a constraint on collision probability (CP). As a departure from previous methods for chance-constrained motion planning, PUMP directly considers both CP and the optimization objective at equal priority when planning through the free configuration space, achieving an unprecedented combination of cost performance, certified safety, and speed. Planning is conducted through a massively parallel multiobjective search, here implemented with a particular application focus on GPU hardware. PUMP explores the configuration space while maintaining a Pareto optimal front of motion plans, considering cost and approximate collision probability. We introduce a novel particle-based CP approximation scheme, designed for efficient GPU implementation, which accounts for dependencies over the history of a trajectory execution. Upon termination of the exploration phase, PUMP performs a search over the Pareto optimal set of solution motion plans to identify the lowest cost motion plan that is certified to satisfy the CP constraint (according to an asymptotically exact estimator). We present numerical experiments for quadrotor planning wherein PUMP identifies solutions in ~100 ms, evaluating over one hundred thousand partial plans through the course of its exploration phase. The results show that this multiobjective search achieves a lower motion plan cost, for the same collision probability constraint, compared to a safety buffer-based search heuristic and repeated RRT trials.
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Real-Time Stochastic Kinodynamic Motion Planning via Multiobjective Search on GPUs, 4.1 out of 5 based on 21 ratings

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